Graph Convolution over Pruned Dependency Trees Improves Relation Extraction

@inproceedings{Zhang2018GraphCO,
  title={Graph Convolution over Pruned Dependency Trees Improves Relation Extraction},
  author={Yuhao Zhang and Peng Qi and Christopher D. Manning},
  booktitle={EMNLP},
  year={2018}
}
Dependency trees help relation extraction models capture long-range relations between words. However, existing dependency-based models either neglect crucial information (e.g., negation) by pruning the dependency trees too aggressively, or are computationally inefficient because it is difficult to parallelize over different tree structures. We propose an extension of graph convolutional networks that is tailored for relation extraction, which pools information over arbitrary dependency… CONTINUE READING
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